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Large Reasoning Models (LRMs), which autonomously produce a reasoning Chain of Thought (CoT) before producing final responses, offer a promising approach to interpreting and monitoring model behaviors. Inspired by the observation that certain CoT patterns -- e.g., ``Wait, did I miss anything?'' -- consistently emerge across tasks, we explore whether LRMs exhibit human-like cognitive habits. Building on Habits of Mind, a well-established framework of cognitive habits associated with successful human problem-solving, we introduce CogTest, a principled benchmark designed to evaluate LRMs' cognitive habits. CogTest includes 16 cognitive habits, each instantiated with 25 diverse tasks, and employs an evidence-first extraction method to ensure reliable habit identification. With CogTest, we conduct a comprehensive evaluation of 16 widely used LLMs (13 LRMs and 3 non-reasoning ones). Our findings reveal that LRMs, unlike conventional LLMs, not only exhibit human-like habits but also adaptively deploy them according to different tasks. Finer-grained analyses further uncover patterns of similarity and difference in LRMs' cognitive habit profiles, particularly certain inter-family similarity

A planet's history dictates its current potential to host habitable conditions and life. The concept of the Continuously Habitable Zone (CHZ) has been used to define the region around a star most likely to host planets with long-term habitability. However, definitions of the CHZ vary in the literature and often conflict with each other. Calculating the fraction of habitable zone planets in the CHZ as a function of stellar properties, we find that the quality of a star as a host for planets with long-term habitability and biosignatures depends strongly on the formulation of the CHZ used. For instance, older M stars are either excellent or sub-optimal hosts for CHZ planets, depending on whether one's definition of habitability prioritizes the total time spent in the habitable zone or the continuity of habitable conditions from the delivery of volatiles to its current age. In this study, we focus on Belatedly Habitable Zone (BHZ) planets, i.e. planets which enter the habitable zone after formation due to the evolution of their host star. We find that between ~29-74% of planets in the habitable zone belong to this class of BHZ planets depending on the timescale for the delivery of vola

Clicks on homepage marketing blocks are driven by a dual-mechanism of content interest and access habits. However, habitual clicks often create Pseudo-Positives in marketing slots, where position advantage masks mediocre content quality, leading to biased recommendation ecosystems. We propose a framework called Orthogonal Disentanglement of Access habits (OrDA) to purify interest signals. OrDA utilizes a dual-tower structure with a gated allocation layer to adaptively route features and minimize interference. To ensure rigorous separation, we employ orthogonal regularization to constrain the latent interest and habit manifolds to be geometrically perpendicular. OrDA performs causal intervention (do-calculus) during inference to rank items solely by purified interest scores. Empirical online evaluations on large-scale datasets demonstrate that OrDA effectively eliminates access-habit bias, outperforming state-of-the-art methods in predictive accuracy. Online AB test 5.64% shows user click-through rates (UCTR) improvement on the Zhima homepage marketing block, Zhima rent-floor recommendation.

We develop a flexible neural demand system for continuous budget allocation that estimates budget shares on the simplex by minimizing KL divergence. Shares are produced via a softmax of a state-dependent preference scorer and disciplined with regularity penalties (monotonicity, Slutsky symmetry) to support coherent comparative statics and welfare without imposing a parametric utility form. State dependence enters through a habit stock defined as an exponentially weighted moving average of past consumption. Simulations recover elasticities and welfare accurately and show sizable gains when habit formation is present. In our empirical application using Dominick's analgesics data, adding habit reduces out-of-sample error by c.33%, reshapes substitution patterns, and increases CV losses from a 10% ibuprofen price rise by about 15-16% relative to a static model. The code is available at https://github.com/martagrz/neural_demand_habit .

Animal motion embodies species-specific behavioral habits, making the transfer of motion across categories a critical yet complex task for applications in animation and virtual reality. Existing motion transfer methods, primarily focused on human motion, emphasize skeletal alignment (motion retargeting) or stylistic consistency (motion style transfer), often neglecting the preservation of distinct habitual behaviors in animals. To bridge this gap, we propose a novel habit-preserved motion transfer framework for cross-category animal motion. Built upon a generative framework, our model introduces a habit-preservation module with category-specific habit encoder, allowing it to learn motion priors that capture distinctive habitual characteristics. Furthermore, we integrate a large language model (LLM) to facilitate the motion transfer to previously unobserved species. To evaluate the effectiveness of our approach, we introduce the DeformingThings4D-skl dataset, a quadruped dataset with skeletal bindings, and conduct extensive experiments and quantitative analyses, which validate the superiority of our proposed model.

The search for life beyond the solar system is a central goal in exoplanetary science. Exoplanet surveys are increasingly detecting potentially habitable exoplanets and large telescopes in space and on ground are aiming to detect possible biosignatures in their atmospheres. At the same time, theoretical studies are expanding the range of habitable environments beyond the conventional focus on Earth-like rocky planets and biosignatures beyond the dominant biogenic gases in the Earth's atmosphere. The present work provides an introductory compendium of key aspects of habitability and biosignatures of importance to the search for life in exoplanetary environments. Basic concepts of planetary habitability are introduced along with essential requirements for life as we know it and the various factors that affect habitability. These include the requirement for liquid water, energy sources, bioessential elements, and geophysical environmental conditions conducive for life. The factors affecting habitability include both astrophysical conditions, such as those due to the host star, as well as planetary processes, such as atmospheric escape, magnetic interactions, and geological activity. A

Large-scale demonstration datasets have been central to recent progress in general-purpose robot policies. However, existing datasets are collected in human-absent settings, and policies trained on such data may perform tasks competently in isolation but fail to exhibit human-aware behaviors. To address this gap, we introduce HABIT, a large-scale robot demonstration dataset for human-present environments. We organize tasks into three roles capturing distinct modes of human-robot interaction: Collaborator, where human and robot jointly accomplish a task; Coworker, where they pursue separate tasks in a shared space; and Supervisor, where the human directs the robot. The dataset comprises over 10K episodes and over 160 hours across 60 tasks. Our experiments show that training on human-present data elicits human-aware behaviors that robot-only data fails to produce: spatiotemporal synchronization in Collaborator tasks, yielding in Coworker tasks, and gesture grounding in Supervisor tasks. Moreover, training on HABIT enables rapid adaptation to new human-robot interaction tasks. By introducing human presence as a new axis of dataset diversity, HABIT extends robot policies to environment

Ice clouds, particularly cirrus clouds, significantly influence Earth's radiative balance but remain poorly characterized in current climate models. A major uncertainty arises from the variability of their microphysical properties, especially the evolution of ice crystal habits under depositional growth. We propose a heuristic method to describe habit evolution based on four fundamental shapes identified in the literature and from in situ observations: droxtals, plates, columns, and rosettes. These represent the primary forms that are relevant under depositional growth, excluding aggregation. In this study, we employ a non-Abelian gauge theory within a field-theoretical framework, imposing an SU(2) $\otimes$ U(1) symmetry on the fields associated with each habit probability growth. This symmetry enables the derivation of a modified system of coupled Fokker-Planck equations, capturing the stochastic growth dynamics of ice crystals while incorporating phenomenological mutual influences among habits. This framework outlines a novel theoretical direction for integrating symmetry principles and field-theoretical tools into the modelling of habit dynamics in ice clouds.

The relative abundances of exotic environments provides us with (uninformed) bounds on the habitability of those environments relative to our own, on the basis that our presence here is not too atypical. For instance, since red stars outnumber yellow stars 7 to 3, we can infer that red stars must be less than 8.1 times as habitable as yellow, as otherwise our presence around a yellow star would be a statistical outlier at the level of $5\%$. In the multiverse context, the relative abundances of exotic environments can be drastically different from those in our universe, which sometimes allows us to place much stronger bounds on their relative habitability than we would get by restricting our attention to our universe. We apply this reasoning to a variety of different exotic environments: tidally locked planets, binary star systems, icy moons, rogue planets, liquids with properties different from water, and waterworlds. We find that the bounds on the relative habitability of rogue planets and waterworlds are at least an order of magnitude stronger in a multiverse context than from our universe alone. Additionally, the belief that some of water's special properties are essential for

Habitability has been generally defined as the capability of an environment to support life. Ecologists have been using Habitat Suitability Models (HSMs) for more than four decades to study the habitability of Earth from local to global scales. Astrobiologists have been proposing different habitability models for some time, with little integration and consistency between them and different in function to those used by ecologists. In this white paper, we suggest a mass-energy habitability model as an example of how to adapt and expand the models used by ecologists to the astrobiology field. We propose to implement these models into a NASA Habitability Standard (NHS) to standardize the habitability objectives of planetary missions. These standards will help to compare and characterize potentially habitable environments, prioritize target selections, and study correlations between habitability and biosignatures. Habitability models are the foundation of planetary habitability science. The synergy between the methods used by ecologists and astrobiologists will help to integrate and expand our understanding of the habitability of Earth, the Solar System, and exoplanets.

Regular exercise is widely recognized as a cornerstone of health, yet sustaining consistent exercise habits remains challenging. Understanding the factors that influence the formation of these habits is crucial for developing effective interventions. This study utilizes data from Mars Athletic Club, Türkiye's largest sports chain, to investigate the dynamics of gym attendance and habit formation. The general problem addressed by this study is identifying the critical periods and factors that contribute to the successful establishment of consistent exercise routines among gym-goers. We show that specific periods of attendance are most crucial for habit formation. By developing a survival metric based on gym attendance patterns, we pinpoint these key phases and segment members into distinct clusters based on their visit patterns. Our analysis reveals significant differences in how various subgroups respond to interventions, such as group classes, personal trainer sessions, and visiting different clubs. Using causal inference analysis, we demonstrate that personalized guidance and social dynamics are key drivers of sustained long-term engagement. By systematically examining these vari

Habitability has been generally defined as the capability of an environment to support life. Ecologists have been using Habitat Suitability Models (HSMs) for more than four decades to study the habitability of Earth from local to global scales. Astrobiologists have been proposing different habitability models for some time, with little integration and consistency among them, being different in function to those used by ecologists. Habitability models are not only used to determine if environments are habitable or not, but they also are used to characterize what key factors are responsible for the gradual transition from low to high habitability states. Here we review and compare some of the different models used by ecologists and astrobiologists and suggest how they could be integrated into new habitability standards. Such standards will help to improve the comparison and characterization of potentially habitable environments, prioritize target selections, and study correlations between habitability and biosignatures. Habitability models are the foundation of planetary habitability science and the synergy between ecologists and astrobiologists is necessary to expand our understandi

Current autonomous driving (AD) simulations are critically limited by their inadequate representation of realistic and diverse human behavior, which is essential for ensuring safety and reliability. Existing benchmarks often simplify pedestrian interactions, failing to capture complex, dynamic intentions and varied responses critical for robust system deployment. To overcome this, we introduce HABIT (Human Action Benchmark for Interactive Traffic), a high-fidelity simulation benchmark. HABIT integrates real-world human motion, sourced from mocap and videos, into CARLA (Car Learning to Act, a full autonomous driving simulator) via a modular, extensible, and physically consistent motion retargeting pipeline. From an initial pool of approximately 30,000 retargeted motions, we curate 4,730 traffic-compatible pedestrian motions, standardized in SMPL format for physically consistent trajectories. HABIT seamlessly integrates with CARLA's Leaderboard, enabling automated scenario generation and rigorous agent evaluation. Our safety metrics, including Abbreviated Injury Scale (AIS) and False Positive Braking Rate (FPBR), reveal critical failure modes in state-of-the-art AD agents missed by p

Primates utilize distributed neural circuits to learn habits in uncertain environments, but the underlying mechanisms remain poorly understood. We propose a formal theory of network energetics explaining how brain states influence sequential behavior. We test our theory on multi-unit recordings from the caudate nucleus and cortical regions of macaques performing a motor habit task. The theory predicts the energy required to transition between brain states represented by trial-specific firing rates across channels, assuming activity spreads through effective connections. We hypothesized that habit formation would correlate with lower control energy. Consistent with this, we observed smaller energy requirements for transitions between similar saccade patterns and those of intermediate complexity, and sessions exploiting fewer patterns. Simulations ruled out confounds from neurons' directional tuning. Finally, virtual lesioning demonstrated robustness of observed relationships between control energy and behavior. This work paves the way for examining how behavior arises from changing activity in distributed circuitry.

The decision to annuitize wealth in retirement planning has become increasingly complex due to rising longevity risk and changing retirement patterns, including increased labor force participation at older ages. While an extensive literature studies consumption, labor, and annuitization decisions, these elements are typically examined in isolation. This paper develops a unified stochastic control and optimal stopping framework in which habit formation and endogenous labor supply shape retirement and annuitization decisions under age-dependent mortality. We derive optimal consumption, labor, portfolio, and annuitization policies in a continuous-time lifecycle model. The solution is characterized via dynamic programming and a Hamilton-Jacobi-Bellman variational inequality. Our results reveal a rich sequence of retirement dynamics. When wealth is low relative to habit, labor is supplied defensively to protect consumption standards. As wealth increases, agents enter a work-to-retire phase in which labor is supplied at its maximum level to accelerate access to retirement. Human capital acts as a stabilizing asset, justifying a more aggressive pre-retirement investment portfolio, followe

Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image through a multimodal query composed of a reference image and modification text. Although this task has demonstrated promising applications in personalized search and recommendation systems, it encounters a severe challenge in practical scenarios known as the Noise Triplet Correspondence (NTC) problem. This issue primarily arises from the high cost and subjectivity involved in annotating triplet data. To address this problem, we identify two central challenges: the precise estimation of composed semantic discrepancy and the insufficient progressive adaptation to modification discrepancy. To tackle these challenges, we propose a cHrono-synergiA roBust progressIve learning framework for composed image reTrieval (HABIT), which consists of two core modules. First, the Mutual Knowledge Estimation Module quantifies sample cleanliness by calculating the Transition Rate of mutual information between the composed feature and the target image, thereby effectively identifying clean samples that align with the intended modification semantics. Second, the Dual-consistenc

This paper provides a dual formulation of the optimal consumption problem with internal multiplicative habit formation. In this problem, the agent derives utility from the ratio of consumption to the internal habit component. Due to this multiplicative specification of the habit model, the optimal consumption problem is not strictly concave and incorporates irremovable path-dependency. As a consequence, standard Lagrangian techniques fail to supply a candidate for the corresponding dual formulation. Using Fenchel's Duality Theorem, we manage to identify a candidate formulation and prove that it satisfies strong duality. On the basis of this strong duality result, we are able to derive duality relations that stipulate how the optimal primal controls depend on the optimal dual controls and vice versa. {Moreover, using the dual formulation, we develop an analytical evaluation mechanism to bound the accuracy of approximations to the optimal solutions.

When searching for inhabited exoplanets, understanding the boundaries of the habitable zone around the parent star is key. If life can strongly influence its global environment, then we would expect the boundaries of the habitable zone to be influenced by the presence of life. Here using a simple abstract model of `tangled-ecology' where life can influence a global parameter, labelled as temperature, we investigate the boundaries of the habitable zone of our model system. As with other models of life-climate interactions, the species act to regulate the temperature. However, the system can also experience `punctuations', where the system's state jumps between different equilibria. Despite this, an ensemble of systems still tends to sustain or even improve conditions for life on average, a feature we call Entropic Gaia. The mechanism behind this is sequential selection with memory which is discussed in detail. With this modelling framework we investigate questions about how Gaia can affect and ultimately extend the habitable zone to what we call the Gaian habitable zone. This generates concrete predictions for the size of the habitable zone around stars, suggests directions for futu

The frequency with which people interact with technology means that users may develop interface habits, i.e. fast, automatic responses to stable interface cues. Design guidelines often assume that interface habits are beneficial. However, we lack quantitative evidence of how the development of habits actually affect user performance and an understanding of how changes in the interface design may affect habit development. Our work quantifies the effect of habit formation and disruption on user performance in interaction. Through a forced choice lab study task (n=19) and in the wild deployment (n=18) of a notificationdialog experiment on smartphones, we show that people become more accurate and faster at option selection as they develop an interface habit. Crucially this performance gain is entirely eliminated once the habit is disrupted. We discuss reasons for this performance shift and analyse some disadvantages of interface habits, outlining general design patterns on how to both support and disrupt them.Keywords: Interface habits, user behaviour, breaking habit, interaction science, quantitative research.